A Pixel-Based Vegetation Greenness Trend Analysis over the Russian Tundra with All Available Landsat Data from 1984 to 2018

Caixia Liu,Huabing Huang,Fangdi Sun

doi:10.3390/rs13234933

Abstract

As Arctic warming continues, its impact on vegetation greenness is complex, variable and inherently scale-dependent. Studies with multiple spatial resolution satellite observations, with 30 m resolution included, on tundra greenness have been implemented all over the North American tundra. However, finer resolution studies on the greenness trends in the Russian tundra have only been carried out at a limited local or regional scale and the spatial heterogeneity of the trend remains unclear. Here, we analyzed the fine spatial resolution dataset Landsat archive from 1984 to 2018 over the entire Russian tundra and produced pixel-by-pixel greenness trend maps with the support of Google Earth Engine (GEE). The entire Russian tundra was divided into six geographical regions based on World Wildlife Fund (WWF) ecoregions. A Theil–Sen regression (TSR) was used for the trend identification and the changed pixels with a significance level p < 0.05 were retained in the final results for a subsequent greening/browning trend analysis. Our results indicated that: (1) the number of valid Landsat observations was spatially varied. The Western and Eastern European Tundras (WET and EET) had denser observations than other regions, which enabled a trend analysis during the whole study period from 1984 to 2018; (2) the most significant greening occurred in the Yamal-Gydan tundra (WET), Bering tundra and Chukchi Peninsula tundra (CT) during 1984–2018. The EET had a greening trend of 2.3% and 6.6% and the WET of 3.4% and 18% during 1984–1999 and 2000–2018, respectively. The area of browning trend was relatively low when we first masked the surface water bodies out before the trend analysis; and (3) the Landsat-based greenness trend was broadly similar to the AVHRR-based trend over the entire region but AVHRR retrieved more browning areas due to spectral mixing adjacent effects. Higher resolution images and field measurement studies are strongly needed to understand the vegetation trend over the Russian tundra ecosystem.

Highlights

We reveal the potential of the Landsat archive for detecting vegetation trends across the Russian tundra but we provide the finest resolution spatially complete trend maps of the entire
As several parts of the Russian tundra (e.g., Middle Siberian Tundra (MST), Chukchi Peninsula tundra (CT) and Eastern Siberian Tundra (EST)) did not have enough satellite observations during 1984–1999, we only showed the trend analysis for 2000–2018
Greenness trends based on the Landsat data indicated that 30.7% and 0.5% of the Russian tundra experienced greening and browning, respectively, at a significance level of p < 0.05 when the water bodies were masked in the first step

Summary

Introduction

The Arctic is warming twice as fast as the global average [1]. Arctic warming has impacts on the tundra ecosystem function because of its interactions with the vegetation cover [2,3,4], wildlife [5,6] and human communities. Understanding the spatial distribution of Arctic greening and browning trends is important to evaluate the Arctic vegetation response to the changing climate or anthropogenic factors [7,8]. Satellite-derived vegetation indices have been used to quantify these changes over high northern latitudes during the past four decades [3,9,10,11,12] with coarse, moderate or high spatial resolution datasets. Increases in vegetation productivity have been completely observed by satellite observations

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Results

Discussion

Conclusion